The present exemplary embodiments relate to a system and method for Artificial Intelligence (AI) planning based quasi-Monte Carlo simulation for probabilistic planning. As observed in the financial markets and other uncertain environments, it is difficult to make rational decisions when the future is unknown. Although there are many ways to create models based on an environment containing an uncertain future, the models need to be solved correctly and completely in order to make optimal decisions with respect to the environment such that losses are prevented or mitigated and gains are maximized. However, the problem of finding optimal solutions within an uncertain environment is normally intractable and at best only approximates solutions with great computation complexity. Thus, the goal is to find an approach that balances between computational complexity and a good quality solution.